1. Field of the Invention
The present invention relates to a prediction parameter analysis apparatus or a prediction parameter analysis method to acquire prediction parameters from an input signal.
2. Description of the Related Art
In a field of audio encoding, LP parameters (linear prediction parameters) are used broadly as spectrum parameters used for expressing the envelope of a spectrum of a signal in speech coding and speech synthesis. An LP parameter analysis performed in the speech coding will be described as an example of prediction parameter analysis.
The conventional prediction parameter analysis is performed as follows.
At first, unnecessary low frequency components affecting analysis of prediction parameters are removed from an input signal by pre-processing. A high frequency pass filter realizes this processing with a cut off frequency of around 50-100 Hz typically. The input signal from which the unnecessary components were removed is windowed by a given time window w(n) to generate a short time input signal x(n) to be used for analysis. The time window is called windowing function or analysis window, and a Humming window is known well. The hybrid window that consists of a first part of half the humming window and a second part of a quarter of a cosine function is used well recently. The hybrid window is adopted in 8 kbit/s speech coding G.729 of an ITU-T recommendation (document 1 “Design and Description of CS-ACELP: A Toll Quality 8 kb/s Speech Coder” IEEE Trans. On Speech and Audio Processing, R. Salami other work, pp. 116-130, Vol. 6, No. 2, March 1998). As thus described, various types of time windows are used according to purpose.
Autocorrelation coefficients Rxx (i) are calculated by the following equation (1) using the short time input signal x(n).                               Rxx          ⁡                      (            i            )                          =                              ∑                          n              =              i                                      L              -              1                                ⁢                                    x              ⁡                              (                n                )                                      ⁢                          x              ⁡                              (                                  n                  -                  i                                )                                                                        (        1        )            where L indicates the length of the time window. The autocorrelation coefficients are referred to as merely ‘autocorrelation’ or ‘autocorrelation function’, but they are substantially the same.
It is performed generally to obtain prediction parameters using the autocorrelation coefficients obtained by the equation (1) or the autocorrelation coefficients subjected to modification by windowing the former autocorrelation coefficients by a fixed lag window. The modification of autocorrelation coefficients using the lag window is referred to the document 1.
A method known as Levinson-Durbin algorithm or recursive solution method of Durbin can be used in a case of obtaining the LP parameters as the prediction parameters. The document 2 “Digital Speech Processing” Tokai university publication meeting, Sadaoki Furui, pp. 75 is referred to in detail.
As thus described, the autocorrelation coefficients of the short time input signal x(n) obtained by windowing the input signal from which the unnecessary low frequency components are removed are calculated in the conventional prediction parameter analysis. However, as shown in waveforms of FIG. 1, the short time input signal cut out from the input signal ((a) in FIG. 1) by the time window is mixed with an unnecessary component (dc component shown by a dashed line in (b) in FIG. 1). Such an unnecessary component increases in case of prediction analysis using the short time window particularly. The unnecessary component affects the analysis of prediction parameters due to tendency to deviate to a low frequency band, resulting in incorrect prediction parameters. Furthermore, degree of mixture of such an unnecessary component varies depending upon the shape and phase of the input signal cut out by the window.
For the above reasons, the conventional prediction parameter analysis includes a problem that it is difficult to obtain the prediction parameters stably.
In the conventional prediction parameter analysis, an unnecessary component (DC component in particular) is mixed in the short time input signal. Therefore, the undesired prediction parameters occur.